22‐4: <i>Invited Paper:</i> OLEDs for Wearables: From Form Factor Engineering to Healthcare Applications

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials’ chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.

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22‐4: Invited Paper: OLEDs for Wearables: From Form Factor Engineering to Healthcare Applications

Cited by 2 publications

References 6 publications

Machine Learning for the Design of Novel OLED Materials

Machine Learning for the Design of Novel OLED Materials

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics

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